A Top-Down Hierarchical Approach for Automatic Indoor Segmentation and Connectivity Detection

Data organization is essential for effective analysis of the spatial relationships between rooms and walls. Segmentation in successive stages plays a crucial role in this process since dividing the data set into smaller sets makes its analysis easier. The proposed approach starts with the segmentation of buildings by storeys using a three-dimensional point cloud and is carried out by detecting peaks in histogram of Z frequency. Subsequently, each storey is segmented into rooms using three-dimensional mathematical morphology techniques, which allows the delimitation of the interior spaces. The third and final step consists of identifying elements within each room, such as doors, ceiling, floor, and walls. During this process, connectivity and adjacency of building elements are studied to automatically derive topological graphs. This methodology results in a deeper and more systematic analysis of three-dimensional spaces, providing a solid basis for the subsequent interpretation and manipulation of the data obtained. The proposed method has been tested in two real cases and the results are shown respectively.